Chronic Treatment with Nigella sativa Oil Exerts Antimanic Properties and Reduces Brain Inflammation in Rats
Bibliographic record
Abstract
(NS) is a native herb consumed habitually in several countries worldwide, possessing manifold therapeutic properties. Among them, anti-inflammatory features have been reported, presumably relating to mechanisms involved in the nuclear factor kappa-B pathway, among others. Given the observed association between neuroimmune factors and mental illness, the primary aim of the present study was to examine the effects of chronic NS use on manic-like behavior in rats, as well as analyze levels of brain inflammatory mediators following NS intake. Using male and female rats, baseline tests were performed; thereafter, rats were fed either regular food (control) or NS-containing food (treatment) for four weeks. Following intervention, behavioral tests were induced (an open field test, sucrose consumption test, three-chamber sociality test, and amphetamine-induced hyperactivity test). Subsequently, brain samples were extracted, and inflammatory mediators were evaluated, including interleukin-6, leukotriene B4, prostaglandin E2, tumor necrosis factor-α, and nuclear phosphorylated-p65. Our findings show NS to result in a marked antimanic-like effect, in tandem with a positive modulation of select inflammatory mediators among male and female rats. The findings reinforce the proposed therapeutic advantages relating to NS ingestion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".